import os
import numpy as np
import pymysql
import pandas as pd
import pymysql.cursors
from sklearn.preprocessing import MinMaxScaler
from joblib import dump

class mysqlUtils(object):
    def __init__(self):
        self.com = pymysql.connect(
            host='127.0.0.1',
            user='root',
            passwd='sjk1234',
            db='sys',
            port=3306,
            charset='utf8'
        )

    def is_holiday(self, data):
        """是否节假日"""
        if data in ['2024-09-03', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-01-01', '2024-01-02', '2024-01-03']:
            return 1
        return 0

    def get_semic_data(self):
        """获取数据"""
        cursor = self.com.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT DATE(g.create_time) as date, HOUR(g.create_time) as hour, count(*) as count
        FROM order_user_gate_rel g
        WHERE HOUR(g.create_time) BETWEEN 6 and 23 
        GROUP BY date, hour
        """

        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)

        # 格式转换
        date_range = pd.date_range(start='2024-07-01', end='2024-12-31', freq='D')
        hours = range(6, 24)
        full_index = pd.MultiIndex.from_product([date_range, hours], names=['date', 'hour'])
        df_full = df.set_index(['date', 'hour']).reindex(full_index, fill_value=0).reset_index()

        # 按天组织数据（每行包含18个小时的检票次数）
        df_pivot = df_full.pivot(index='date', columns='hour', values='count')
        df_pivot['dow'] = df_pivot.index.dayofweek  # 星期几（0-6）
        df_pivot['month'] = df_pivot.index.month  # 月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)

        # 对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month'], dtype=int)

        # 对小时列进行归一化
        hours_columns = list(range(6, 24))
        df_hours = df_pivot[hours_columns].copy()

        feature_columns = [col for col in df_pivot.columns if col not in hours_columns]
        df_feature = df_pivot[feature_columns].copy()

        scaler = MinMaxScaler()
        scaled_hours = scaler.fit_transform(df_hours)
        dump(scaler, 'scaler.joblib')

        # 将归一化后的数据转化为DataFrame
        df_hours_scaled = pd.DataFrame(scaled_hours, columns=hours_columns, index=df_hours.index)

        # 合并
        df_pivot_clean = pd.concat([df_hours_scaled, df_feature], axis=1)
        print(df_pivot_clean.head)

        # 获取当前文件的目录
        current_dir = os.path.dirname(os.path.abspath(__file__))
        output_file = os.path.join(current_dir, 'scenic_data.csv')

        # 保存文件
        df_pivot_clean.to_csv(output_file, index=False)
        print(f"Data saved to {output_file}")

if __name__ == '__main__':
    mu = mysqlUtils()
    mu.get_semic_data()